Data service providers offer data access to users through networks. Many applications are increasingly using multiresolutional data. Multiresolutional data often includes image data represented at different levels of detail. Metadata may also be included depending upon a particular application. Such image data can include, but is not limited to, geographic information data (e.g. data representing imagery, map, terrain), and image data (such as photos captured or compressed at different resolutions). Such multiresolutional data can increase storage costs for data service providers because of the different levels of detail involved. Many applications using multiresolutional data may demand reliable services even for a large number of users over a large geographical area.
For example, Google Inc. provides street-level imagery of roads and streets worldwide to Internet users. The data may have multiple copies with different levels of detail. Each copy at a given level of detail may be further broken into one or more data objects, each of which contains a portion of the copy. To maintain a reliable data service, data is usually replicated over multiple data servers and data centers. For example, a data object may be replicated on several data servers in each data center that serves this service. A data object may have multiple copies even though it is rarely accessed and the information it provides may be useful only to a small number of users. The total size of multiresolutional data alone can be in the range of petabytes (PBs). When this multiresolutional data is replicated, this can require storage space in the range of high hundreds of PBs to exabytes (EBs). Such storage requirement is costly and makes increasing the scale of a data service very expensive. To decrease storage cost, some data service providers choose to have low levels of replication for all data objects. This may result in low user happiness however, because important data items are often unavailable or user access may be delayed.